Pharmaceutical Stock Price Reactions to Price Constraint Threats and Firm-Level R&D Spending

Political pressure in the United States is again building to constrain pharmaceutical prices either directly or through legalized reimportation of lower-priced pharmaceuticals from foreign countries. This study uses the Clinton Administration's Health Security Act (HSA) of 1993 as a natural experiment to show how threats of price constraints affect firm-level R&D spending. We link events surrounding the HSA to pharmaceutical company stock price changes and then examine the cross-sectional relation between the stock price changes and subsequent unexpected R&D spending changes. Results show that the HSA had significant negative effects on firm stock prices and R&D spending. Conservatively, the HSA reduced R&D spending by $1.6 billion, even though it never became law. If the HSA had passed, and had many small firms not raised capital just prior to the HSA, the R&D effects could have been much larger.

Financial Contagion and Market Liquidity: Evidence from the Asian Crisis

Models of financial crisis and contagion predict that an economic crisis turns into a crisis of market liquidity in the presence of borrowing constraints, information asymmetry and risk aversion. Based on the firm-level data on a sample of exposed and unexposed US stocks to the Asian currency crisis, we find a significant increase (decrease) in the crisis period bid-ask spreads (depth) and their volatilities for both the groups. While our results underscore the imprints of flight to quality, we detect little causal patterns in liquidity innovations. An important implication of our findings, as evidenced by the recent crisis, is that regulatory response to enhance liquidity during a crisis should not be limited to the industries and markets directly exposed to the crisis. Finally, we find that the deterioration in market liquidity provides a partial explanation for the crisis-induced abnormal returns

The waiting period of initial public offerings

The length of time it takes an IPO firm to go public (called ‘waiting period’) reflects multiple layers of scrutiny from underwriters, auditors, venture capitalists, institutional investors, and regulators. Accordingly, we show that the waiting period is a good barometer of ex ante uncertainty about future cash flows and that it has predictive power after the firm goes public. We find that firms marked by short waiting periods experience lower underpricing and less uncertainty and superior stock/operating performance in the aftermarket. We also report that smaller firms are taking longer to go public after SOX Act, thus providing justification for the 2012 JOBS Act

Policy Uncertainty and Firm Cash Holdings

This research examines the relation between government economic policy uncertainty and firm cash holdings. We find evidence that policy uncertainty is positively related to firm cash holdings due to firms’ precautionary motives and, to a lesser extent, investment delays. The relation between policy uncertainty and cash holdings is more pronounced for firms dependent on government spending and extends beyond business cyclicality. Further analysis indicates that the effects of policy uncertainty on corporate cash holdings are distinct from those of political, market, or other macroeconomic uncertainty

Quantifying the agreement between computational models and experimental data under uncertainty

Bound-to-bound data collaboration (abbreviated B2BDC) is a deterministic optimization-based approach for uncertainty quantification. The framework combines models and data from multiple sources by formulating inequality constraints over a parameter space. This dissertation explores the following question: how can agreement between computational models and experimental data be quantified while necessarily accounting for uncertainty in both model parameters and observations? In a typical B2BDC application, this is performed by constructing a dataset -- a collection of constraints over an uncertain parameter space involving surrogate models, experimental data, and prior knowledge -- and then assessing its consistency. Our first contribution is a formalization of this procedure within an iterative context. This new strategy effectively extends the applicability of the B2BDC technique and can be viewed as a natural extension of previous work. Oftentimes, demonstrating model-data disagreement is just as important as verifying agreement. In B2BDC, this is manifested through dataset inconsistency. Our second contribution is a new tool for analyzing inconsistency called the vector consistency measure. This measure provides a more thorough diagnosis of an inconsistent dataset by computing minimal constraint corrections that lead to consistency. The inclusion of weights facilitates domain expert knowledge and opinions to be incorporated in the process of resolving an inconsistency. The primary developments in this thesis are methodological. Their application is illustrated on various examples, ranging from the small-scale instances drawn from the literature to larger-scale realistic gas combustion datasets

Quantifying the agreement between computational models and experimental data under uncertainty

Bound-to-bound data collaboration (abbreviated B2BDC) is a deterministic optimization-based approach for uncertainty quantification. The framework combines models and data from multiple sources by formulating inequality constraints over a parameter space. This dissertation explores the following question: how can agreement between computational models and experimental data be quantified while necessarily accounting for uncertainty in both model parameters and observations? In a typical B2BDC application, this is performed by constructing a dataset -- a collection of constraints over an uncertain parameter space involving surrogate models, experimental data, and prior knowledge -- and then assessing its consistency. Our first contribution is a formalization of this procedure within an iterative context. This new strategy effectively extends the applicability of the B2BDC technique and can be viewed as a natural extension of previous work. Oftentimes, demonstrating model-data disagreement is just as important as verifying agreement. In B2BDC, this is manifested through dataset inconsistency. Our second contribution is a new tool for analyzing inconsistency called the vector consistency measure. This measure provides a more thorough diagnosis of an inconsistent dataset by computing minimal constraint corrections that lead to consistency. The inclusion of weights facilitates domain expert knowledge and opinions to be incorporated in the process of resolving an inconsistency. The primary developments in this thesis are methodological. Their application is illustrated on various examples, ranging from the small-scale instances drawn from the literature to larger-scale realistic gas combustion datasets

Board compensation practices and agency costs of debt

Extant theory and empirical evidence indicate that equity-based compensation can align the interests of managers with those of shareholders, but it has a side effect of aggravating bondholder-shareholder conflicts by increasing managers' risk-shifting incentives. Recent evidence confirms that extending equity-based compensation to outside directors also is effective in aligning their interests with those of shareholders, but its adverse effects on the debt-related agency problems are unknown. In this paper, we examine how stock and stock option compensation for outside directors affects corporate bond yields in the secondary market. Our results show that the greater the ratio of outside directors' stock and option compensation to total compensation, the lower the average yield spreads on the firms' outstanding bonds, with stock compensation having a larger impact than option compensation. Further, the effect of equity-based compensation on yield spreads is stronger for firms with lower-rated debt.Director incentives Cost of debt Corporate governance Agency costs